qwen25-7b-sft-merged-v5v6-a50/README.md

base_model, datasets, language, license, library_name, pipeline_tag, tags

base_model	datasets	language	license	library_name	pipeline_tag	tags
Qwen/Qwen2.5-7B-Instruct	u-10bei/sft_alfworld_trajectory_dataset_v2 u-10bei/sft_alfworld_trajectory_dataset_v3 u-10bei/sft_alfworld_trajectory_dataset_v4 u-10bei/sft_alfworld_trajectory_dataset_v5 u-10bei/dbbench_sft_dataset_react u-10bei/dbbench_sft_dataset_react_v2 u-10bei/dbbench_sft_dataset_react_v3 u-10bei/dbbench_sft_dataset_react_v4	en	apache-2.0	transformers	text-generation	sft agent tool-use alfworld dbbench

qwen25-7b-sft-merged-v5v6-a50

This repository provides a fully merged model fine-tuned from Qwen2.5-7B-Instruct using QLoRA + Unsloth.

Two SFT models (v5 and v6) were trained independently, then combined via weight interpolation (alpha=0.5). This is a complete model — no adapters or additional weights are needed.

Training Objective

This model is trained to improve multi-turn agent task performance on ALFWorld (household tasks) and DBBench (database operations).

Loss is applied to all assistant turns in the multi-turn trajectory, enabling the model to learn environment observation, action selection, tool use, and recovery from errors.

Training Configuration

• Base model: Qwen/Qwen2.5-7B-Instruct
• Method: QLoRA (4-bit) + Unsloth, merged into base model
• Max sequence length: 2048
• Epochs: 2
• Learning rate: 5e-5
• LoRA: r=32, alpha=64
• Post-training: weight interpolation of v5 and v6 (alpha=0.5)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "plotMaker/qwen25-7b-sft-merged-v5v6-a50"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

References

• Model Soups (Wortsman et al., 2022) — Weight interpolation of fine-tuned models
• LoRA (Hu et al., 2021) — Low-Rank Adaptation
• NEFTune (Jain et al., 2024) — Noisy embedding fine-tuning
• rsLoRA (Kalajdzievski, 2023) — Rank-stabilized LoRA scaling
• ALFWorld (Shridhar et al., 2021) — Interactive text-world environments
• ReAct (Yao et al., 2023) — Reasoning and acting in LLMs

Sources & Terms (IMPORTANT)

Training data:

• u-10bei/sft_alfworld_trajectory_dataset_v2 ~ v5
• u-10bei/dbbench_sft_dataset_react ~ v4

Base model: Qwen/Qwen2.5-7B-Instruct

This repository does NOT redistribute the dataset. Users must comply with the dataset license and base model terms.